Method And System For Attaching A Metatag To A Digital Image

ABSTRACT

A system and method for tagging an image of an individual in a plurality of photos is disclosed herein. A feature vector of an individual is used to analyze a set of photos on a social networking website such as www.facebook.com to determine if an image of the individual is present in a photo of the set of photos. Photos having an image of the individual are tagged preferably by listing a URL or URI for each of the photos in a database.

CROSS REFERENCE TO RELATED APPLICATION

The Present application is a continuation application of U.S. patentapplication Ser. No. 13/753,543, filed on Jan. 30, 2013, which is acontinuation application of U.S. patent application Ser. No. 12/341,318,filed on Dec. 22, 2008, now U.S. Pat. No. 8,369,570, issued on Feb. 5,2013, which claims priority to U.S. Provisional Patent No. 61/016,800,filed on Dec. 26, 2007, now abandoned, and is a continuation-in-partapplication of U.S. patent application Ser. No. 11/534,667, filed onSep. 24, 2006, now U.S. Pat. No. 7,450,740, issued on Nov. 11, 2008,which claims priority to U.S. Provisional Patent Application No.60/721,226, filed Sep. 28, 2005, now abandoned, all of which are herebyincorporated by reference in their entireties.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a method and system for meta-tagging acollection of digital photos containing an image of an individual orindividuals.

2. Description of the Related Art

Classification of facial images using feature recognition software iscurrently used by various government agencies such as the Department ofHomeland Security (DHS) and the Department of Motor Vehicles (DMV) fordetecting terrorists, detecting suspected cases of identity fraud,automating border and passport control, and correcting mistakes in theirrespective facial image databases. Facial images stored in the DMV orDHS are digitized and stored in centralized databases, along withassociated information on the person. Examples of companies that providebiometric facial recognition software include Cross Match Technologies,Cognitec, Cogent Systems, and Iridian Technologies; of these, Cognitecalso provides a kiosk for digitally capturing images of people forstorage into their software.

Your face is an important part of who you are and how people identifyyou. Imagine how hard it would be to recognize an individual if allfaces looked the same. Except in the case of identical twins, the faceis arguably a person's most unique physical characteristic. While humanshave had the innate ability to recognize and distinguish different facesfor millions of years, computers are just now catching up.

Visionics, a company based in New Jersey, is one of many developers offacial recognition technology. The twist to its particular software,FACEIT, is that it can pick someone's face out of a crowd, extract thatface from the rest of the scene and compare it to a database full ofstored images. In order for this software to work, it has to know what abasic face looks like. Facial recognition software is based on theability to first recognize faces, which is a technological feat initself, and then measure the various features of each face.

If you look in the mirror, you can see that your face has certaindistinguishable landmarks. These are the peaks and valleys that make upthe different facial features. Visionics defines these landmarks asnodal points. There are about 80 nodal points on a human face. A few ofthe nodal points that are measured by the FACEIT software: distancebetween eyes; width of nose; depth of eye sockets; cheekbones; Jaw line;and chin. These nodal points are measured to create a numerical codethat represents the face in a database. This code is referred to as afaceprint and only fourteen to twenty-two nodal points are necessary forthe FACEIT software to complete the recognition process.

Facial recognition methods may vary, but they generally involve a seriesof steps that serve to capture, analyze and compare your face to adatabase of stored images. The basic process that is used by the FACEITsoftware to capture and compare images is set forth below and involvesDetection, Alignment, Normalization, Representation, and Matching. Toidentify someone, facial recognition software compares newly capturedimages to databases of stored images to see if that person is in thedatabase.

Detection is when the system is attached to a video surveillance system,the recognition software searches the field of view of a video camerafor faces. If there is a face in the view, it is detected within afraction of a second. A multi-scale algorithm is used to search forfaces in low resolution. The system switches to a high-resolution searchonly after a head-like shape is detected.

Alignment is when a face is detected, the system determines the head'sposition, size and pose. A face needs to be turned at least thirty-fivedegrees toward the camera for the system to register the face.

Normalization is when the image of the head is scaled and rotated sothat the head can be registered and mapped into an appropriate size andpose. Normalization is performed regardless of the head's location anddistance from the camera. Light does not impact the normalizationprocess.

Representation is when the system translates the facial data into aunique code. This coding process allows for easier comparison of thenewly acquired facial data to stored facial data.

Matching is when the newly acquired facial data is compared to thestored data and linked to at least one stored facial representation.

The heart of the FACEIT facial recognition system is the Local FeatureAnalysis (LFA) algorithm. This is the mathematical technique the systemuses to encode faces. The system maps the face and creates thefaceprint. Once the system has stored a faceprint, it can compare it tothe thousands or millions of faceprints stored in a database. Eachfaceprint is stored as an 84-byte file.

One of the first patents related to facial recognition technology isRothfjell, U.S. Pat. No. 3,805,238 for a Method For IdentifyingIndividuals using Selected Characteristics Body Curves. Rothfjellteaches an identification system in which major features (e.g. the shapeof a person's nose in profile) are extracted from an image and stored.The stored features are subsequently retrieved and overlaid on a currentimage of the person to verify identity.

Another early facial recognition patent is Himmel, U.S. Pat. No.4,020,463 for an Apparatus And A Method For Storage And Retrieval OfImage Patterns. Himmel discloses digitizing a scanned image into binarydata which is then compressed and then a sequence of coordinates andvector values are generated which describe the skeletonized image. Thecoordinates and vector values allow for compact storage of the image andfacilitate regeneration of the image.

Yet another is Gotanda, U.S. Pat. No. 4,712,103 for a Door Lock ControlSystem. Gotanda teaches, inter alia, storing a digitized facial image ina non-volatile ROM on a key, and retrieving that image for comparisonwith a current image of the person at the time he/she request access toa secured area. Gotanda describes the use of image compression, by asmuch as a factor of four, to reduce the amount of data storage capacityneeded by the ROM that is located on the key.

Yet another is Lu, U.S. Pat. No. 4,858,000. Lu teaches an imagerecognition system and method for identifying ones of a predeterminedset of individuals, each of whom has a digital representation of his orher face stored in a defined memory space.

Yet another is Tal, U.S. Pat. No. 4,975,969. Tal teaches an imagerecognition system and method in which ratios of facial parameters(which Tal defines a distances between definable points on facialfeatures such as a nose, mouth, eyebrow etc.) are measured from a facialimage and are used to characterize the individual. Tal, like Lu in U.S.Pat. No. 4,858,000, uses a binary image to find facial features.

Yet another is Lu, U.S. Pat. No. 5,031,228. Lu teaches an imagerecognition system and method for identifying ones of a predeterminedset of individuals, each of whom has a digital representation of his orher face stored in a defined memory space. Face identification data foreach of the predetermined individuals are also stored in a UniversalFace Model block that includes all the individual pattern images or facesignatures stored within the individual face library.

Still another is Burt, U.S. Pat. No. 5,053,603. Burt teaches an imagerecognition system using differences in facial features to distinguishone individual from another. Burt's system uniquely identifiesindividuals whose facial images and selected facial feature images havebeen learned by the system. Burt's system also “generically recognizes”humans and thus distinguishes between unknown humans and non-humanobjects by using a generic body shape template.

Still another is Turk et al., U.S. Pat. No. 5,164,992. Turk teaches theuse of an Eigenface methodology for recognizing and identifying membersof a television viewing audience. The Turk system is designed to observea group of people and identify each of the persons in the group toenable demographics to be incorporated in television ratingsdeterminations.

Still another is Deban et al., U.S. Pat. No. 5,386,103. Deban teachesthe use of an Eigenface methodology for encoding a reference face andstoring said reference face on a card or the like, then retrieving saidreference face and reconstructing it or automatically verifying it bycomparing it to a second face acquired at the point of verification.Deban teaches the use of this system in providing security for AutomaticTeller Machine (ATM) transactions, check cashing, credit card securityand secure facility access.

Yet another is Lu et al., U.S. Pat. No. 5,432,864. Lu teaches the use ofan Eigenface methodology for encoding a human facial image and storingit on an “escort memory” for later retrieval or automatic verification.Lu teaches a method and apparatus for employing human facial imageverification for financial transactions.

Technologies provided by wireless carriers and cellular phonemanufacturers enable the transmission of facial or object images betweenphones using Multimedia Messaging Services (MMS) as well as to theInternet over Email (Simple Mail Transfer Protocol, SMTP) and WirelessAccess Protocol (WAP). Examples of digital wireless devices capable ofcapturing and receiving images and text are camera phones provided byNokia, Motorola, LG, Ericsson, and others. Such phones are capable ofhandling images as JPEGs over MMS, Email, and WAP across many of thewireless carriers: Cingular, T-Mobile, (GSM/GPRS), and Verizon (CDMA)and others.

Neven, U.S. Patent Publication 2005/0185060, for an Image Base Inquirysystem For Search Engines For Mobile Telephones With Integrated Camera,discloses a system using a mobile telephone digital camera to send animage to a server that converts the image into symbolic information,such as plain text, and furnishes the user links associated with theimage which are provided by search engines.

Neven, et al., U.S. Patent Publication 2006/0012677, for an Image-BasedSearch Engine For Mobile Phones With Camera, discloses a system thattransmits an image of an object to a remote server which generates threeconfidence values and then only generates a recognition output from thethree confidence values, with nothing more. I

Adam et al., U.S. Patent Publication 2006/0050933, for a Single ImageBased Multi-Biometric System And Method which integrates face, skin andiris recognition to provide a biometric system.

The general public has a fascination with celebrities and many membersof the general public use celebrities as a standard for judging someaspect of their life. Many psychiatrists and psychologists believe theconfluence of forces coming together in technology and media have led tothis celebrity worship factor in our society. One output of thiscelebrity factor has been a universal approach to compare or determinethat someone looks like a certain celebrity. People are constantlystating that someone they meet or know looks like a celebrity, whetherit is true or not. What would be helpful would be to scientificallyprovide a basis for someone to lay claim as looking like a certaincelebrity.

BRIEF SUMMARY OF THE INVENTION

The present invention provides a novel method and system for taggingdigital photos containing an image of an individual or individuals. Thesystem and method can be used to organize a collection of digital photosposted on a social networking web site such as facebook.com.

A feature vector is created for an individual from one or more digitalphotos. This feature vector is then utilized to search a database ofdigital photos and tag those digital photos containing an image of theindividual as determined by a match to the feature vector. The databaseof digital photos may be stored on a social networking web site such asmyspace.com or www.facebook.com. The meta-tagging of a digital photocomprises marking the location (X-Y coordinate in the digital photo,size in the digital photo, and tilt in the digital photo) and storingthis facial location information along with a unique identificationreference for the individual, and a unique identification reference forthe digital photo (this can be a URL or URI for the digital photo if ona web site). The actual digital photo is not modified in any manner.

The digital image is preferably captured by a wireless communicationdevice (preferably a mobile telephone) or from a personal computer (PC).The image is preferably in a JPEG, TIFF, GIF or other standard imageformat. Further, an analog image may be utilized if digitized. The imageis sent to the wireless carrier and subsequently sent over the internetto an image classification server. Alternatively, the digital image maybe uploaded to a PC from a digital camera or scanner and then sent tothe image classification server over the internet.

After an image is received by the image classification server, the imageis processed into a feature vector, which reduces the complexity of thedigital image data into a small set of variables that represent thefeatures of the image that are of interest for classification purposes.

The feature vector is compared against existing feature vectors in animage database to find the closest match. The image database preferablycontains one or more feature vectors for each target individual.

The digital photo used for the creating a feature vector may be createdin a number of different methods. The user may capture a digital imagewith a digital camera enabled wireless communication device, such as amobile telephone. The compressed digital image is sent to the wirelesscarrier as a multimedia message (MMS), a short message service (“SMS”),an e-mail (Simple Mail Transfer Protocol (“SMTP”)), or wirelessapplication protocol (“WAP”) upload. The image is subsequently sent overthe internet using HTTP or e-mail to an image classification server.Alternatively, the digital image(s) may be uploaded to a PC from adigital camera, or scanner. Once on the PC, the image(s) can betransferred over the internet to the image classification server as ane-mail attachment, or HTTP upload.

After the image is received by the image classification server, afeature vector is generated for the image. A feature vector is a smallset of variables that represent the features of the image that are ofinterest for classification purposes. Creation and comparison offeatures vectors may be queued, and scaled across multiple machines.Alternatively, different feature vectors may be generated for the sameimage. Alternatively, the feature vectors of several images of the sameindividual may be combined into a single feature vector. The incomingimage, as well as associate features vectors, may be stored for laterprocessing, or added to the image database. For faces, possible featurevector variables are the distance between the eyes, the distance betweenthe center of the eyes, to the chin, the size, and shape of theeyebrows, the hair color, eye color, facial hair if any, and the like.

One aspect of the present invention is a method for tagging an image ofan individual in a plurality of photos. The method includes providing afirst plurality of photos. Each of the first plurality of photoscomprises an identified image of the individual. The method alsoincludes processing the image of the individual in each of the firstplurality of photos to generate a feature vector for the individual. Themethod also includes analyzing a second plurality of photos to determineif an image of the individual is present in a photo of the secondplurality of photos. The analysis comprises determining if an image ineach of the photos of the second plurality of photos matches the featurevector for the individual. The method also includes identifying each ofthe photos of the second plurality of photos having an image of theindividual to create a third plurality of photos. The method alsoincludes tagging each of the photos of the third plurality of photos toidentify the image of the individual in each of the third plurality ofphotos.

Another aspect of the present invention is a system for tagging an imageof an individual in a plurality of photos. The system includes anetwork, a database, a server engine, a second plurality of photos on asocial networking web site, analysis means, identification means andtagging means. The database comprises a first plurality of photos of animage of an individual. The server engine processes the first pluralityof photos to generate a feature vector for the image of the individual.The analysis means analyzes the second plurality of photos to determineif an image of the individual is present in a photo of the secondplurality of photos. The analysis comprises determining if an image ineach of the photos of the second plurality of photos matches the featurevector for the individual. The identification means identifies each ofthe photos of the second plurality of photos having an image of theindividual to create a third plurality of photos. The tagging means tagseach of the photos of the third plurality of photos to identify theimage of the individual in each of the third plurality of photos.

Yet another aspect of the present invention is a method for meta-taggingan image of an individual in a plurality of photos. The method includesproviding a first plurality of photos. Each of the first plurality ofphotos comprises an identified image of the individual. The method alsoincludes processing the image of the individual in each of the firstplurality of photos to generate a feature vector for the individual. Themethod also includes analyzing a second plurality of photos to determineif an image of the individual is present in a photo of the secondplurality of photos. The analysis comprises determining if an image ineach of the photos of the second plurality of photos matches the featurevector for the individual. The method also includes identifying each ofthe photos of the second plurality of photos having an image of theindividual to create a third plurality of photos. The method alsoincludes meta-tagging each of the photos of the third plurality ofphotos to identify the image of the individual in each of the thirdplurality of photos.

Yet another aspect of the present invention is a method for tagging animage of an individual in a plurality of photos. The method includescreating a feature vector for an individual from a plurality ofreference photos. The method also includes analyzing a second pluralityof photos to determine if an image of the individual is present in aphoto of the second plurality of photos. The analysis comprisesdetermining if an image in each of the photos of the second plurality ofphotos matches the feature vector for the individual. The method alsoincludes identifying each of the photos of the second plurality ofphotos having an image of the individual to create a third plurality ofphotos. The method also includes determining the location the locationof the image in the photo. The method also includes storing the locationof the image in the photo in a database. The method also includesstoring an identifier for the photo in a database. The method alsoincludes storing an identifier for the individual in a database.

Having briefly described the present invention, the above and furtherobjects, features and advantages thereof will be recognized by thoseskilled in the pertinent art from the following detailed description ofthe invention when taken in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a schematic diagram of a system of the present invention.

FIG. 2 is a digital photo with a facial image of an individual.

FIG. 3 is the facial image of FIG. 2 with feature vector indicators.

FIG. 4 is an illustration of digital photos from a social networkingwebsite.

FIG. 4A is an illustration of one of the digital photos from FIG. 4 withlocation information illustrated.

FIG. 5 is a flow chart of a specific method of the present invention.

FIG. 6 is a flow chart of a specific method of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

A system of the present invention is generally illustrated in FIG. 1. Auser 55 can use a mobile telephone to transmit an image or images over anetwork to a processing server 65 for processing. Alternatively, theuser 55 can use a computer to transmit the images to the processingserver 65 over the internet. As discussed in more detail below, theprocessing server 65 creates a feature vector from the image or images.The processing server can then access a database of digital photos 75 todetermine if any of the digital photos can the image. If the databasephotos contain the image, information is generated for each image andstored on a database 70.

Generally, a facial image is transmitted over a network to an imageclassification server or processing server, preferably over a wirelessnetwork. The facial image is preferably sent over the internet usingHTTP or e-mail to the image classification server. The facial image,preferably a compressed digital facial image such as a JPEG image, issent to a wireless carrier as a MMS, a SMS, a SMTP, or WAP upload.Alternatively, the facial image is uploaded to a computer from a digitalcamera, or scanner and then transferred over the internet to the imageclassification server as an e-mail attachment, or HTTP upload.

The facial image is analyzed at the image classifications server todetermine if the facial image is of adequate quality to be processed formatching. Quality issues with the facial image include but are notlimited to a poor pose angle, brightness, shading, eyes closed,sunglasses worn, obscured facial features, or the like. Processing ofthe image preferably comprises using an algorithm which includes aprinciple component analysis technique to process the face of the facialimage into an average of a multitude of faces, otherwise known as theprinciple component and a set of images that are the variance from theaverage face image known as the additional components. Each isreconstructed by multiplying the principal components and the additionalcomponents against a feature vector and adding the resulting imagestogether. The resulting image reconstructs the original face of thefacial image. Processing of the facial image comprises factors such asfacial hair, hair style, facial expression, the presence of accessoriessuch as sunglasses, hair color, eye color, and the like. Essentially aprimary feature vector is created for the facial image. This primaryfeature vector is compared to a plurality of database of imagespreferably located on a social networking website. A more detaileddescription of generating feature vectors is disclosed in Shah, et al.,U.S. Pat. No. 7,450,740, for an Image Classification And InformationRetrieval Over Wireless Digital Networks And The Internet, which ishereby incorporated by reference in its entirety.

The present invention preferably uses facial recognition softwarecommercially or publicly available such as the FACEIT brand softwarefrom IDENTIX, the FACEVACS brand software from COGNETIC, and others.Those skilled in the pertinent art will recognize that there are manyfacial recognition softwares, including those in the public domain, thatmay be used without departing from the scope and spirit of the presentinvention.

The operational components of the image classification server/processingserver 65 preferably include an input module, transmission engine, inputfeed, feature vector database, sent images database, facial recognitionsoftware, perception engine, and output module. The input module isfurther partitioned into wireless device inputs, e-mail inputs and HTTP(internet) inputs.

A digital photo 100 of a facial image of an individual is shown in FIG.2. The digital photo is sent to the processing server for creation of afeature vector for this individual. The feature vector is generatedbased on facial features, and this allows the image of the individual tobe distinguished within other digital photos. Such features include thehair color 102, face shape 104, distance between eyes 106, hair style108, distance between eyes and mouth 110, length of mouth 112 and noseshape 114, and other like features. The primary feature vector is thenused to identify other digital photos bearing an image of theindividual. As shown in FIG. 4, a collection of digital photos bearingan image of the individual are identified. In FIG. 4A, a particularphoto bearing an image of the individual is analyzed for locationinformation which is preferably stored in a database 70. An X-Y positionof the image is determined, along with the size of the image and tiltangle. This allows image to be quickly identified.

A method 400 for tagging an image of an individual in a plurality ofphotos is shown in FIG. 5. In this method, at block 402, a first set ofdigital photos is provided with each of the digital photos containing animage of an individual. The first set of photos is preferably providedto a processing server over a network. At block 404, the image or imagesof the individual is/are processed, preferably at the processing server,to generate a feature vector for the image(s) of the individual. Atblock 406, a second set of photos is analyzed, preferably by the server,to determine if any of the photos of the second set of photos has animage that matches the feature vector. The second set of photos ispreferably located on a social networking website, such as myspace.com,or facebook.com. At block 408, photos of the second set of photos thatcontain an image that matches the feature vector are identified,preferably by the processing server. At block 410, these identifiedphotos are tagged to create a third set of photos.

A method 500 for tagging a facial image of an individual in a pluralityof digital photos, is shown in FIG. 6. For example, a user may want tocreate links to unorganized digital photos bearing an image of anindividual or group of individuals. The present method allows the userto create such links. At block 502, a feature vector for a facial imageof an individual is created at a processing server. The feature vectoris preferably created from a first set of photos containing the facialimage of the individual. At block 504, a second set of digital photos isanalyzed, preferably by the processing server, to determine if any ofthe digital photos of the second set of photos has a facial image thatmatches the feature vector. The second set of photos is preferablylocated on a social networking website, such as myspace.com, orfacebook.com. At block 506, photos of the second set of photos thatcontain an image that matches the feature vector are identified,preferably by the processing server. At block 508, the locationinformation of the facial image in each of the second set of digitalphotos is determined by the processing server. The location informationis preferably the X and Y coordinates, the size of the facial image andthe tilt angle of the facial image in the digital photo. At block 510,an identifier and the location information of the facial image for eachof the identified digital photos is stored on a database, preferably atthe processing server.

From the foregoing it is believed that those skilled in the pertinentart will recognize the meritorious advancement of this invention andwill readily understand that while the present invention has beendescribed in association with a preferred embodiment thereof, and otherembodiments illustrated in the accompanying drawings, numerous changesmodification and substitutions of equivalents may be made thereinwithout departing from the spirit and scope of this invention which isintended to be unlimited by the foregoing except as may appear in thefollowing appended claim. Therefore, the embodiments of the invention inwhich an exclusive property or privilege is claimed are defined in thefollowing appended claims.

We claim as our invention the following:
 1. A method, for tagging animage of an individual, comprising: storing on a server supporting aservice usable by multiple users through respective remote usercomputing devices accessing the service over a distributed network aplurality of reference photos comprising identified facial images ofindividual users of the service and, for each of a plurality ofidentified facial images, an identity of the individual and at least onereference feature vector generated from the facial images to provide aplurality of stored reference feature vectors, the server comprising atleast one processor that accesses at least one storage media and beingprogrammed with executable instructions; processing, by the server, anunknown facial image of an individual in a subject photo to generate asubject feature vector for the individual in the subject photo;determining, by the server, coordinates defining a position of theunknown facial image in the subject photo; determining, by the server,an identity of the unknown facial image, wherein the determiningcomprises comparing the subject feature vector to one or more storedreference feature vectors using a matching algorithm; and upondetermining an identity of the unknown facial image, tagging, by theserver, the subject photo to identify the facial image of theindividual, wherein the tagging comprises storing in a storage media thecoordinates defining a position of the facial image in the subject photoand an identifier for the individual, the coordinates and identifier forthe individual being accessible by a computing device when the computingdevice accesses the subject photo.
 2. The method of claim 1, whereineach feature vector comprises at least one variable that represents afeature of a corresponding facial image.
 3. The method of claim 1,further comprising storing an identifier for the subject photo.
 4. Themethod of claim 3, wherein the identifier for the subject photocomprises a URL or URI.
 5. The method of claim 3, further comprisingstoring data representing a size of the facial image of the individualin the subject photo.
 6. The method of claim 1, further comprisingtransmitting to a computing device information for display of thesubject photo and of an identity of the facial image of the individualin the subject photo at a position associated with the storedcoordinates.
 7. The method of claim 1, wherein the step of determiningan identity of the unknown facial image further comprises selecting areference feature vector that is most similar to the subject featurevector.
 8. The method of claim 7, wherein the step of determining anidentity of the unknown facial image further comprising transmitting toa computing device an identity of an individual associated with theselected reference feature vector for display with the subject photo andreceiving human perception feedback regarding the identity of theunknown facial image from a user of the service.
 9. The method of claim1, wherein the step of determining an identity of an unknown facialimage comprises applying a statistical model for comparison of featurevectors.
 10. The method of claim 9, wherein the statistical model isupdated based on human perception feedback from users of the service.11. The method of claim 9, wherein at least one feature vector used inthe statistical model is weighted based on human perception feedbackfrom a user of the service.
 12. The method of claim 8, wherein the stepof determining an identity of an unknown facial image comprises applyinga statistical model for comparison of feature vectors, and wherein thestatistical model is updated based on the human perception feedbackregarding the identity of the unknown facial image.
 13. The method ofclaim 10, 11 or 12, wherein the statistical model is represented by aneural network, support vector machine or Bayesian network.
 14. Themethod of claim 1, wherein the plurality of reference photos comprisesmultiple photos of at least one individual in different poses and facialexpressions.
 15. The method of claim 1, further comprising, upondetermining an identity of the unknown facial image, storing the subjectfeature vector in association with an identifier for the individual forsubsequent use in identifying a facial image of the individual in one ormore other photos.
 16. The method of claim 15, further comprisingcombining the subject feature vector and at least one reference featurevector of the individual for subsequent use in identifying a facialimage of the individual in one or more other photos.
 17. The method ofclaim 15, further comprising storing data representing a tilt angle ofthe facial image in the subject photo in association with the subjectfeature vector for subsequent use in identifying a facial image of theindividual in one or more photos.
 18. The method of claim 1, theplurality of reference feature vectors including at least one featurevector based on a plurality of reference photos.
 19. The method of claim1, wherein the subject photo is received by the server from a computerover the internet, or from a mobile telephone over a wireless network.20. The method of claim 1, further comprising analyzing, by the server,the unknown facial image of the subject photo to determine if theunknown facial image is acceptable.
 21. The method of claim 1, furthercomprising processing the unknown facial image, by the server, tonormalize the image to an appropriate scale and rotation.
 22. The methodof claim 1, wherein the service is a social networking service.
 23. Amethod, for tagging an image of an individual, comprising: storing on aserver supporting a service usable by multiple users through respectiveremote user computing devices accessing the service over a distributednetwork a plurality of reference photos comprising identified facialimages of individual users of the service and, for each of a pluralityof identified facial images, an identity of the individual and at leastone reference feature vector generated from the facial images to providea plurality of stored reference feature vectors, the server comprisingat least one processor that accesses at least one storage media andbeing programmed with executable instructions; processing, by theserver, an unknown facial image of an individual in a subject photo togenerate a subject feature vector for the individual in the subjectphoto; determining, by the server, coordinates defining a position ofthe unknown facial image in the subject photo; determining, by theserver, an identity of the unknown facial image, wherein the determiningcomprises: comparing the subject feature vector to each of a pluralityof stored reference feature vectors using a statistical model andselecting a reference feature vector that is most similar to the subjectfeature vector; transmitting to a computing device an identity of anindividual associated with the selected reference feature vector fordisplay with the subject photo; receiving human perception feedbackregarding the identity of the unknown facial image from a user of theservice; and upon determining an identity of the unknown facial image,tagging, by the server, the subject photo to identify the facial imageof the individual, wherein the tagging comprises storing in a storagemedia the coordinates defining a position of the facial image in thesubject photo and an identifier for the individual, the coordinates andidentifier for the individual being accessible by a computing devicewhen the computing device accesses the subject photo.
 24. The method ofclaim 23, wherein each feature vector comprises at least one variablethat represents a feature of a corresponding facial image.
 25. Themethod of claim 23, further comprising storing an identifier for thesubject photo.
 26. The method of claim 25, wherein the identifier forthe subject photo comprises a URL or URI.
 27. The method of claim 23,wherein the statistical model is updated based on human perceptionfeedback from users of the service.
 28. The method of claim 23, whereinthe statistical model is updated based on the human perception feedbackregarding the identity of the unknown facial image.
 29. The method ofclaim 23, wherein at least one feature vector used in the statisticalmodel is weighted based on human perception feedback from a user of theservice.
 30. The method of claim 27, 28 or 29, wherein the statisticalmodel is represented by a neural network, support vector machine orBayesian network.
 31. The method of claim 23, further comprising, upondetermining an identify of the unknown facial image, storing the subjectfeature vector in association with an identifier for the individual forsubsequent use in identifying a facial image of the individual in one ormore other photos.
 32. The method of claim 31, further comprisingcombining the subject feature vector and at least one reference featurevector of the individual for subsequent use in identifying a facialimage of the individual in one or more other photos.
 33. The method ofclaim 23, wherein, the subject photo is received by the server from acomputer over the internet, or from a mobile telephone over a wirelessnetwork.
 34. The method of claim 23, wherein the service is a socialnetworking service.
 35. A method, for tagging an image of an individual,comprising: storing on a server supporting a service usable by multipleusers through respective remote user computing devices accessing theservice over a distributed network a plurality of reference photoscomprising identified facial images of individual users of the serviceand, for each of a plurality of identified facial images, an identity ofthe individual and at least one reference feature vector generated fromthe facial images to provide a plurality of stored reference featurevectors, the server comprising at least one processor that accesses atleast one storage media and being programmed with executableinstructions; processing, by the server, an unknown facial image of anindividual in a subject photo to generate a subject feature vector forthe individual in the subject photo; determining, by the server, anidentity of the unknown facial image, wherein the determining comprisescomparing the subject feature vector to each of a plurality of storedreference feature vectors and selecting a reference feature vector thatis most similar to the subject feature vector; and upon determining anidentity of the unknown facial image of the individual, storing in astorage media the subject feature vector in association with anidentifier of the individual for subsequent use in identifying a facialimage of the individual in one or more other photos, and tagging, by theserver, the subject photo to identify the facial image of theindividual, wherein the tagging comprises storing in a storage media anidentifier for the individual, the identifier for the individual beingaccessible by a computing device when the computing device accesses thesubject photo.
 36. The method of claim 35, further comprising combining,by the server, the selected reference feature vector and the subjectfeature vector to form a combined reference feature vector for theindividual for use in identifying a facial image of the individual inone or more other photos.
 37. The method of claim 35 or 36, furthercomprising storing in a storage media an identifier for the subjectphoto.
 38. The method of claim 37, wherein the identifier for thesubject photo comprises a URL or URI.
 39. The method of claim 35,further comprising storing in a storage media data representing a sizeof the facial image in the subject photo.
 40. The method of claim 35 or36, further comprising determining, by the server, coordinates defininga position of the unknown facial image in the subject photo and storingthe coordinates in a storage media, the coordinates and identifier forthe individual being associated with the subject photo and accessible bya computing device when the computing device accesses the subject photo.41. The method of claim 40, further comprising transmitting to acomputing device information for displaying an identity of an individualin the subject photo with the subject photo at a position associatedwith the stored coordinates.
 42. The method of claim 35, wherein thestep of determining an identity of an unknown facial image comprisesapplying a statistical model for comparison of feature vectors.
 43. Themethod of claim 42, wherein the statistical model is updated based onhuman perception feedback from users of the service.
 44. The method ofclaim 42, wherein at least one feature vector used in the statisticalmodel is weighted based on human perception feedback from a user of theservice.
 45. The method of claim 35, wherein the step of determining anidentity of the unknown facial image further comprising transmitting toa computing device information for display with the subject photo anidentity of an individual associated with the selected reference featurevector and receiving human perception feedback regarding the identity ofthe unknown facial image from a user of the service.
 46. The method ofclaim 45, wherein the step of determining an identity of an unknownfacial image comprises applying a statistical model for comparison offeature vectors, and wherein the statistical model is updated based onthe human perception feedback regarding the identity of the unknownfacial image.
 47. The method of claim 43, 44, 45 or 46, wherein thestatistical model is represented by a neural network, support vectormachine or Bayesian network.
 48. The method of claim 35, the pluralityof reference feature vectors including at least one feature vector basedon a plurality of reference photos.
 49. The method of claim 35, whereinthe subject photo is received by the server from a computer over theinternet, or from a mobile telephone over a wireless network.
 50. Themethod of claim 35, further comprising analyzing, by the server, theunknown facial image of the subject photo to determine if the unknownfacial image is acceptable.
 51. The method of claim 35, wherein theservice is a social networking service.
 52. A system for tagging animage of an individual in a photo, the system comprising: a data storeaccessible by a server supporting a social networking web site andcomprising a plurality of reference feature vectors generated from aplurality of processed facial images of identified individuals; a serverengine configured to implement computer programming to perform thefollowing: receive a subject photo containing an unknown facial image ofan individual; generate a subject feature vector from the unknown facialimage; determine a predicted identity of the unknown facial image basedon a statistical comparison model that analyzes the subject featurevector relative to the plurality of reference feature vectors; transmitthe predicted identity over a network to a computing device for displaywith the subject photo; receive perception feedback regarding thepredicted identity from a user of the social network; and tag thesubject photo to identify the facial image of the individual in thesubject photo, wherein the tagging comprises storing in a the data storean identifier of the subject photo, an identifier of the individual inthe subject photo based on the perception feedback, and coordinatesdefining a position of the facial image of the individual in the subjectphoto.
 53. The system of claim 52, wherein each feature vector comprisesat least one variable that represents a feature of a correspondingfacial image.
 54. The system of claim 52, wherein the identifier for thesubject photo comprises a URL or URI.
 55. The system of claim 52,wherein the server is configured to update the statistical comparisonmodel based on human perception feedback from users of the socialnetworking service.
 56. The system of claim 55, wherein the server isconfigured to update the statistical model based on the human perceptionfeedback regarding the predicted identity of the unknown facial image.57. The system of claim 52, wherein the server is configured to weigh atleast one feature vector used in the statistical model based on humanperception feedback from a user of the social networking service. 58.The system of claim 55, 56 or 57, wherein the statistical model isrepresented by a neural network, support vector machine or Bayesiannetwork.
 59. The system of claim 52, wherein the server is configured tostore the subject feature vector in association with an identifier ofthe individual in the subject photo for subsequent use in identifying afacial image of the individual in one or more other photos.
 60. Thesystem of claim 59, wherein the server is configured to combine thesubject feature vector and at least one reference feature vector of theindividual for subsequent use in identifying a facial image of theindividual in one or more other photos.
 61. The system of claim 52,wherein the server is configured to receive the subject phototransmitted from a computer over the internet, or transmitted from amobile telephone over a wireless network to the server.
 62. An systemfor tagging an image of an individual in a photo, comprising: storagesupporting a service, the storage storing a plurality of referencephotos comprising identified facial images of individual users of theservice and, for each of a plurality of identified facial images, anidentity of the individual and at least one reference feature vectorgenerated from the facial images; at least one networked serversupporting a service usable by multiple users through respective remoteuser computing devices accessing the service over a distributed network,wherein the at least one networked server has access to the storage andis configured to implement computer programming to perform thefollowing: receive a subject photo containing a unknown facial image ofan individual; process the subject photo to generate a feature vectorfor the unknown facial image and to determine coordinates of a positionof the unknown facial image in the subject photo; determine whether theunknown facial image in the subject photo corresponds to an individualin a reference photo via a comparison of the feature vector of theunknown facial image with the plurality of stored reference featurevectors; and upon determination that the unknown facial image in thesubject photo corresponds to an individual in a reference photo, tag thesubject photo by causing the storage of the coordinates of the unknownfacial image of the subject photo and an identifier for the individualin at least one database accessible by the server, wherein thecoordinates of the unknown facial image of the subject photo and anidentifier for the individual stored in the at least one database areaccessible by one or more of the remote user computing devices whenaccessing the subject photo.
 63. The system of claim 62, wherein the atleast one networked server is configured to implement computerprogramming to detect the unknown facial image of an individual in thesubject photo.
 64. The system of claim 62, wherein the at least onenetworked server is configured to effect storing a URL or URI for thesubject photo.
 65. The system of claim 62, wherein the at least onenetworked server is configured to transmit the coordinates and theidentifier for the unknown facial image of the subject photo to one ormore remote user computing devices for displaying an identity of theindividual with the subject photo at a position in the subject photoassociated with the stored coordinates.
 66. The system of claim 62,wherein each feature vector comprises at least one variable thatrepresents a feature of a corresponding facial image.
 67. The system ofclaim 62, wherein the at least one networked server is configured toselect a reference feature vector that is most similar to the subjectfeature vector.
 68. The system of claim 67, wherein the at least onenetworked server is configured to transmit to a computing device anidentity of an individual associated with the selected reference featurevector for display with the subject photo and to receive humanperception feedback regarding the identity of the unknown facial imagefrom a user of the service.
 69. The system of claim 62, wherein the atleast one networked server is configured to apply a statistical modelfor comparison of feature vectors to determine whether the unknownfacial image in the subject photo corresponds to an individual in areference photo.
 70. The system of claim 69, wherein the at least onenetworked server is configured to update the statistical model based onhuman perception feedback from users of the service.
 71. The system ofclaim 69, wherein the at least one networked server is configured toweigh at least one feature vector used in the statistical model based onhuman perception feedback from a user of the service.
 72. The system ofclaim 69, wherein the at least one networked server is configured toupdate the statistical model based on the human perception feedbackregarding the identity of the unknown facial image.
 73. The system ofclaim 70, 71 or 72, wherein the statistical model is represented by aneural network, support vector machine or Bayesian network.
 74. Thesystem of claim 62, wherein the at least one networked server isconfigured to store the subject feature vector in association with anidentifier for the individual upon tagging the subject photo forsubsequent use in identifying a facial image of the individual in one ormore other photos.
 75. The system of claim 74, wherein the at least onenetworked server is configured to combine the subject feature vector andat least one reference feature vector of the individual for subsequentuse in identifying a facial image of the individual in one or more otherphotos.
 76. The system of claim 74, wherein the at least one networkedserver is configured to store data representing a tilt angle of thefacial image in the subject photo in association with the subjectfeature vector for subsequent use in identifying a facial image of theindividual in one or more photos.
 77. The system of claim 62, whereinthe at least one networked server is configured to receive the subjectphoto transmitted from a computer over the internet, or transmitted froma mobile telephone over a wireless network to the server.
 78. The systemof claim 62, wherein the at least one networked server is configured toanalyzing the unknown facial image of the subject photo to determine ifthe unknown facial image is acceptable.
 79. The system of claim 62,wherein the at least one networked server is configured to normalize theimage to an appropriate scale and rotation.
 80. The system of claim 62,wherein the service is a social networking service.